In the present age, networks of computing devices, for example internet, have become a popular and important medium for carrying out various day-to-day activities. These activities include the use of network based social media for people to people interaction, online marketing, advertisements, entertainment, blogs, tweets and file sharing etc. Any medium that involves interaction between two or more network users and allow such activities have now started to come under the classification of social media, and it is increasingly becoming more popular, organized and effective with time. The common elements of social media would include, but is not limited to, social networks, chatting tools and forums, messaging services, web logs, personal pages, user review sites, deal aggregators, gaming consoles, discussion forums, file sharing interfaces, online magazines, online surveys and blogs etc. These elements are now being used widely by users to maximize their visibility, draw feedback, research product and marketing ideas, reach potential customers, address existing customers and resolve issues.
One of the key components emerging out of network based social media is the data analytics of various social media activity data and metadata recorded, processed and categorized which is made available in respect of the various interactions made in social media and associated metadata. The said social media activity data and metadata could be at an individual user level or multiple user level or group level. There are certain diagnostic tools available which use social media activity data to generate analytics output such as reports, trends, patterns, frequency charts etc. which are used for analysing and understanding the performance of a social media element. These are normally used by businesses or individuals in understanding the user behaviour, perception, interest and feedback, which is further by way of manual interpretation used to derive actionable points to increase or improve the quality and quantity of interaction with users or prospective users of the social media element. The correct and effective interpretation of the analytics output is significant to the success of social media elements and needs to be done in a systematic and logical manner, for which effective systems and methods are required. In the current state of art, there are no effective systems or methods which use the analytics output and interpret them to derive actionable points to increase or improve the quality and quantity of social media interaction. The interpretation which is often done by humans could be erroneous, incomplete, unreasonable and limited, and if the same can be done by an automated mechanism comprising predefined algorithms, parameters and formula, then such interpretation is likely to be far more accurate and effective.
Another drawback of the current practices in interpretation methods of social media analytics is that while such interpretation and consequent actionable points are deduced, certain related factors which are not directly forming a part of the analytics data and metadata are not considered and thus can lead to less effective and logical actionable points. Examples of such factors include industry dynamics, socio-political factors, seasonal patterns etc.
In terms of social media elements and platforms, dedicated pages, sites, tools, applications, interfaces, or the like, can be established for leveraging a business in terms of internet presence and related advertisements. But there is no system in the prior art which recommends a user or a page owner to modify content in order to increase effectiveness of its social media presence in order to provide better a user-visitor engagement quotient.
The present invention in its various embodiments, aims to address the above drawbacks and requirements, and provide effective systems and methods for providing effective interpretation and actionable points in the form of automated recommendations for social media activity.